Comparison
flash-linear-attention vs Liger-Kernel
Verdict
Pick flash-linear-attention if flash-linear-attention accelerates linear attention mechanisms in large language models, using CUDA for optimal performance; pick Liger-Kernel if optimized Triton kernels for accelerating LLM training, especially on ROCm PyTorch installations.
Markdown twin · flash-linear-attention alternatives · Liger-Kernel alternatives
GraphCanon updated Sep 20, 2026
12views this month
vs
Trust & integrity
| Signal | flash-linear-attention | Liger-Kernel |
|---|---|---|
| Maintenance | Very active (0d since push) As of Aug 17, 2026 · github_public_v1 | Very active (3d since push) As of Sep 7, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Aug 17, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 7, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- flash-linear-attention
- 🚀 Efficient implementations for emerging model architectures
- Liger-Kernel
- Efficient Triton Kernels for LLM Training
Stars
- flash-linear-attention
- 5.6k
- Liger-Kernel
- 6.6k
Forks
- flash-linear-attention
- 661
- Liger-Kernel
- 593
Open issues
- flash-linear-attention
- 98
- Liger-Kernel
- 215
Language
- flash-linear-attention
- Python
- Liger-Kernel
- Python
Adopt for
- flash-linear-attention
- Flash-linear-attention accelerates linear attention mechanisms in large language models, using CUDA for optimal performance.
- Liger-Kernel
- Optimized Triton kernels for accelerating LLM training, especially on ROCm PyTorch installations.
Persona
- flash-linear-attention
- -
- Liger-Kernel
- -
Runtime
- flash-linear-attention
- -
- Liger-Kernel
- -
License
- flash-linear-attention
- MIT
- Liger-Kernel
- BSD-2-Clause
Last pushed
- flash-linear-attention
- Aug 17, 2026
- Liger-Kernel
- Sep 3, 2026
Categories
- flash-linear-attention
- Model Training
- Liger-Kernel
- Model Training
Trust and health
Days since push
- flash-linear-attention
- 0d
- Liger-Kernel
- 3d
Open issues (now)
- flash-linear-attention
- 98
- Liger-Kernel
- 215
Stars delta
- flash-linear-attention
- +208 (30d)
- Liger-Kernel
- +45 (30d)
Open issues delta
- flash-linear-attention
- +21 (30d)
- Liger-Kernel
- +25 (30d)
Full report
- flash-linear-attention
- Trust report
- Liger-Kernel
- Trust report
Shared compatibility
- Python · flash-linear-attention: Python runtime · Liger-Kernel: Python runtime
Choose flash-linear-attention if…
- License: flash-linear-attention is MIT, Liger-Kernel is BSD-2-Clause.
- Tags unique to flash-linear-attention: large-language-models, machine-learning-systems, natural language processing, sequence-modeling.
- High-performance requirements with Nvidia GPUs where CUDA can offer significant speed-ups
When NOT to use flash-linear-attention
- Limited GPU hardware or no support for backend flavors like CUDA, ROCM, XPU, NPU, or CPU
- Do not require linear attention mechanism in modeling large language models or sequence data
Choose Liger-Kernel if…
- License: Liger-Kernel is BSD-2-Clause, flash-linear-attention is MIT.
- Tags unique to Liger-Kernel: finetuning, gemma2, llama, mistral.
- When enhancing training speed of large language models with ROCm-compatible hardware.
When NOT to use Liger-Kernel
- Avoid if only CUDA environments are supported, as Liger-Kernel emphasizes ROCm compatibility.
- Skip for simple setup requirements; prefer more streamlined tools without extensive customization options.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (fla-org/flash-linear-attention) · observed Sep 20, 2026
- GitHub forks (fla-org/flash-linear-attention) · observed Sep 20, 2026
- Last push (fla-org/flash-linear-attention) · observed Aug 17, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (linkedin/Liger-Kernel) · observed Sep 20, 2026
- GitHub forks (linkedin/Liger-Kernel) · observed Sep 20, 2026
- Last push (linkedin/Liger-Kernel) · observed Sep 3, 2026
- License file (BSD-2-Clause) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: flash-linear-attention 5.6k · Liger-Kernel 6.6k (synced Sep 20, 2026).
Common questions
- What is the difference between flash-linear-attention and Liger-Kernel?
- flash-linear-attention: 🚀 Efficient implementations for emerging model architectures. Liger-Kernel: Efficient Triton Kernels for LLM Training. See the comparison table for live GitHub stats and shared categories.
- When should I choose flash-linear-attention over Liger-Kernel?
- Choose flash-linear-attention over Liger-Kernel when License: flash-linear-attention is MIT, Liger-Kernel is BSD-2-Clause; Tags unique to flash-linear-attention: large-language-models, machine-learning-systems, natural language processing, sequence-modeling; High-performance requirements with Nvidia GPUs where CUDA can offer significant speed-ups.
- When should I choose Liger-Kernel over flash-linear-attention?
- Choose Liger-Kernel over flash-linear-attention when License: Liger-Kernel is BSD-2-Clause, flash-linear-attention is MIT; Tags unique to Liger-Kernel: finetuning, gemma2, llama, mistral; When enhancing training speed of large language models with ROCm-compatible hardware.
- When should I avoid flash-linear-attention?
- Limited GPU hardware or no support for backend flavors like CUDA, ROCM, XPU, NPU, or CPU Do not require linear attention mechanism in modeling large language models or sequence data
- When should I avoid Liger-Kernel?
- Avoid if only CUDA environments are supported, as Liger-Kernel emphasizes ROCm compatibility. Skip for simple setup requirements; prefer more streamlined tools without extensive customization options.
- Is flash-linear-attention or Liger-Kernel more popular on GitHub?
- Liger-Kernel has more GitHub stars (6,600 vs 5,568). Stars measure visibility, not whether either tool fits your constraints.
- Are flash-linear-attention and Liger-Kernel open source?
- Yes - both are open-source projects on GitHub (flash-linear-attention: MIT, Liger-Kernel: BSD-2-Clause).
- Where can I find alternatives to flash-linear-attention or Liger-Kernel?
- GraphCanon lists graph-backed alternatives at flash-linear-attention alternatives and Liger-Kernel alternatives (flash-linear-attention markdown twin, Liger-Kernel markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, flash-linear-attention or Liger-Kernel?
- flash-linear-attention: Very active. Liger-Kernel: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for flash-linear-attention and Liger-Kernel?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: flash-linear-attention trust report; Liger-Kernel trust report.